Fuzzy term partition identification

ABSTRACT

A method, computer system, and a computer program product for building and applying fuzzy term partitions is provided. The present invention may include building a fuzzy category taxonomy. The present invention may also include implementing the built fuzzy category taxonomy into a fuzzy category classifier. The present invention may then include building a fuzzy term extractor. The present invention may further include building a fuzzy term association map. The present invention may also include processing a plurality of words stored on a database. The present invention may then include extracting a fuzzy term from the processed plurality of words. The present invention may further include associating the extracted fuzzy term with a plurality of context data. The present invention may also include producing a context data partition for the extracted fuzzy term. The present invention may then include applying a weight to the extracted fuzzy term.

BACKGROUND

The present invention relates generally to the field of computing, andmore particularly to linguistic variables. Linguistic variables may beused in conjunction with a cognitive system based on fuzzy rules.Linguistic variables may also be known as fuzzy terms. People use fuzzyterms in everyday conversations and fuzzy terms may be defineddifferently depending on various factors. The ability to identify afuzzy term and the corresponding fuzzy term meaning into a crisp valuemay provide feedback for a person communicating using fuzzy terms.

SUMMARY

Embodiments of the present invention disclose a method, computer system,and a computer program product for building and applying fuzzy termpartitions. The present invention may include receiving a fuzzy terminput. The present invention may also include building a fuzzy categorytaxonomy based on the received input. The present invention may theninclude implementing the built fuzzy category taxonomy into a fuzzycategory classifier. The present invention may further include buildinga fuzzy term extractor based on the implemented fuzzy categoryclassifier. The present invention may also include building a fuzzy termassociation map based on the built fuzzy term extractor. The presentinvention may then include processing a plurality of words stored on adatabase. The present invention may further include extracting a fuzzyterm from the processed plurality of words. The present invention mayalso include associating the extracted fuzzy term with a plurality ofcontext data. The present invention may then include producing a contextdata partition for the extracted fuzzy term based on the associatedplurality of context data. The present invention may further includeapplying a weight to the extracted fuzzy term.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

These and other objects, features and advantages of the presentinvention will become apparent from the following detailed descriptionof illustrative embodiments thereof, which is to be read in connectionwith the accompanying drawings. The various features of the drawings arenot to scale as the illustrations are for clarity in facilitating oneskilled in the art in understanding the invention in conjunction withthe detailed description. In the drawings:

FIG. 1 illustrates a networked computer environment according to atleast one embodiment;

FIGS. 2A and 2B are operational flowcharts illustrating a process forbuilding fuzzy term partitions according to at least one embodiment;

FIG. 3 is an operational flowchart illustrating a process for applyingfuzzy term partitions according to at least one embodiment;

FIG. 4 is a block diagram of internal and external components ofcomputers and servers depicted in FIG. 1 according to at least oneembodiment;

FIG. 5 is a block diagram of an illustrative cloud computing environmentincluding the computer system depicted in FIG. 1, in accordance with anembodiment of the present disclosure; and

FIG. 6 is a block diagram of functional layers of the illustrative cloudcomputing environment of FIG. 5, in accordance with an embodiment of thepresent disclosure.

DETAILED DESCRIPTION

Detailed embodiments of the claimed structures and methods are disclosedherein; however, it can be understood that the disclosed embodiments aremerely illustrative of the claimed structures and methods that may beembodied in various forms. This invention may, however, be embodied inmany different forms and should not be construed as limited to theexemplary embodiments set forth herein. Rather, these exemplaryembodiments are provided so that this disclosure will be thorough andcomplete and will fully convey the scope of this invention to thoseskilled in the art. In the description, details of well-known featuresand techniques may be omitted to avoid unnecessarily obscuring thepresented embodiments.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language, python programminglanguage or similar programming languages. The computer readable programinstructions may execute entirely on the user's computer, partly on theuser's computer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through any type of network, includinga local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer (for example, through theInternet using an Internet Service Provider). In some embodiments,electronic circuitry including, for example, programmable logiccircuitry, field-programmable gate arrays (FPGA), or programmable logicarrays (PLA) may execute the computer readable program instructions byutilizing state information of the computer readable programinstructions to personalize the electronic circuitry, in order toperform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The following described exemplary embodiments provide a system, methodand program product for building and applying fuzzy term partitions tofuzzy terms widely used in conversations. As such, the presentembodiment has the capacity to improve the technical field of cognitivecomputing by detecting and extracting fuzzy terms from everydayconversations. More specifically, machine learning may be used to buildand implement a fuzzy term partition program by associating fuzzy termsin a relevant context for a user to have the ability to ask a computingdevice, either verbally or by typing, a question using fuzzy terms andto be able to receive a response relevant to the user.

As previously described, linguistic variables may be used in conjunctionwith a cognitive system based on fuzzy rules. Linguistic variables mayalso be known as fuzzy terms. People use fuzzy terms in everydayconversations and fuzzy terms may be defined differently depending onvarious factors. The ability to identify a fuzzy term and thecorresponding fuzzy term meaning into a crisp value may provide feedbackfor a person communicating using fuzzy terms.

A fuzzy term used in conversation may, for example, include terms suchas hot weather, slow traffic, low cost, early morning or a tall person.The fuzzy terms in the preceding example are hot, slow, low, early andtall. Fuzzy terms are used and widely accepted in society. Fuzzy terms,however, may differ depending on factors (i.e., contexts) of theconversation such as location, region, culture, demographic, personalpreferences, group preferences or age of the individuals incommunication. Since fuzzy term values may be dependent based oncontext, mapping a fuzzy term to a crisp value may provide differentcrisp value results. A crisp value may be a specific value associatedwith the context of an individual. For example, an individual who livesin a northern region may use the fuzzy term, cold weather, where coldrelates to a category of weather, and the crisp value associated withthe northern region individual may be a range of −10° Celsius (C) to −5°C. Alternatively, an individual who lives in a southern region may usethe same fuzzy term, cold weather, and the crisp value associated withthe southern region individual may be 10° C. to 15° C.

Mapping fuzzy terms used in a verbal utterance (e.g., in a sentence, ina conversation) or a type written utterance (e.g., text message, emailmessage or a social media post) may provide different crisp valuesdepending on the context of an individual or the context of theindividual's utterance. Therefore, it may be advantageous to, amongother things, provide a method for detecting and extracting fuzzy termsfrom conversations to identify fuzzy term values and associate theidentified fuzzy terms with various usage contexts.

According to at least one embodiment, a fuzzy term partition program mayidentify a fuzzy term from a conversation (e.g., a social mediaconversation or a verbal conversation) and correlate the fuzzy term to afuzzy category (i.e., category) to be associated with a fuzzy partition(i.e., partition or fuzzy term partition). A partition may include acategory, a fuzzy term, an attribute, context data and crisp values.Categories may include, for example, the weather, a product, a routedescription, the time of day, traffic or a description of a person.Fuzzy terms may include, for example, hot, warm, cold, cheap, expensive,close, far, early, late, rush-hour, congested, traffic jam, bumper tobumper, tall, short or young. Attributes may include, for example,temperature, pricing, distance, feet, miles, meters, density, height orage. Context data may include, for example, location, region, state,city, culture, demographic, personal preferences, group preferences orage of the individuals in communication. Crisp values may be exactvalues associated with the fuzzy terms as the fuzzy terms relate to thecategory, attribute and context data, such as a price range, atemperature range, a range in distance, or an age range.

The fuzzy term partition may relate to the crisp value of a fuzzy termand the crisp value may be dependent on the category, the attribute andthe context of the fuzzy term. For example, a crisp value may includerange of temperature degrees when conversing about the weather for aparticular region or a range of prices when conversing about a productsold in a particular location. The context of a fuzzy term may also beconsidered a category. Each fuzzy term may have an associated crispvalue range based on various contexts (e.g., location, region, culture,demographic, personal preferences, group preferences or age). Each fuzzyterm, therefore, may have multiple fuzzy partitions (i.e., crisp values)for each context.

A fuzzy term partition program may build partitions for differentcategories and then may apply the built partitions, for example, whenexecuting the program for a user. Building partitions may begin byextracting and using stored conversation data from a particular databasefor a particular corpus. Building partitions may also begin byreceiving, processing and identifying fuzzy terms in conversations andutterances from individuals who may be speaking into a device (e.g., asmart phone, a smart watch, a tablet or a computing device with amicrophone). Conversations and utterances may also come from anindividual posting type-written messages (e.g., text message, emailmessage or a social media post). Natural language processing (NLP) maybe used to identify a fuzzy term and to identify the category of a fuzzyterm. For example, social media analytics may be used in conjunctionwith NLP to identify fuzzy terms and to categorize fuzzy terms.

From a conversation or an utterance, crisp values may be extracted fromthe identified and processed fuzzy terms to associate each fuzzy termwith a crisp value of relevant context data. For example, the fuzzy termcold is categorized as weather and the real temperature for theparticular region of the person in the conversation may be representedby the crisp value (e.g., temperature range considered to be cold for aperson living in the particular region). The fuzzy term partitionprogram may then produce updates relevant to the context fuzzypartitions. Different weights may be applied to the extracted fuzzy termvalues based on, for example, the individual's expertise level withregard to the referenced fuzzy term. An expertise level may, forexample, be associated with an expert in a related field, such as asubject matter expert (SME) or may be a person who has lived in aparticular region for an extended period of time.

The produced fuzzy partitions may be used by an information retrievalsystem for mapping fuzzy terms to crisp values. The produced fuzzypartitions may also be used in a recommendation or expert system forproviding more accurate and relevant recommendations to a user. Theproduced fuzzy partitions may further be used in an expert system thatmay be based on fuzzy logic rules. One system or service that the fuzzyterm partition program may communicate with may include IBM Watson®Natural Language Classifier (IBM Watson Natural Language Classifier andall IBM Watson Natural Language Classifier-based trademarks and logosare trademarks or registered trademarks of International BusinessMachines Corporation and/or its affiliates). One other system or servicethe fuzzy term partition program may communicate with may include IBMWatson® Conversation Services (IBM Watson Conversation Services and allIBM Watson Conversation Services-based trademarks and logos aretrademarks or registered trademarks of International Business MachinesCorporation and/or its affiliates).

Fuzzy term partitions, for example, may be identified from social mediaconversations and thereafter, the conversations may be mapped to thecontext of usage for a fuzzy term. The fuzzy term partition program maythen build a measure to identify the term for the particular context ofusage and identify the value (e.g., crisp value) that may be deduced bythe content of the conversation. For example, in a conversation when aperson is discussing the weather, the fuzzy term partition program mayobtain the real value for the weather at the time of the conversation bymapping the origin (i.e., location) of the person in the conversationwith a weather data application. The real value (i.e., crisp value) ofthe used fuzzy term may be used to identify fuzzy boundaries (e.g., forthe category of weather, a boundary of −10° C. to −5° C.).

Boundaries may satisfy a fuzzy term for each different context, since afuzzy term may be used in more than one context. If a fuzzy term hasmore than one context, then the same fuzzy term may have more than oneboundary. A conversation may be built by boundaries that satisfy thefuzzy term to the boundaries for each different context. For example, acomputation system may identify a fuzzy term, then the computationsystem may retrieve a crisp value based on the identified concept.Context may be defined with attributes for mapping fuzzy terms to thepartition that may be relevant to the specific context.

An association may be built and mapped for each fuzzy term. Theassociation may be a kind of context the fuzzy term value may be deducedfrom and the crisp value that the fuzzy term partition program hascollected. Once the fuzzy term partition program builds the association,the more data that is supplied (i.e., more data provided to a corpus ora database) may result in more attributes. A fuzzy term partitionprogram may build the association map to map fuzzy terms with contextattributes relevant to a particular fuzzy term category. The categorycontext may have a defined set of attributes used to build partitions.At runtime (i.e., the time the program is executing), the correspondingpartitions may be extracted using context attributes. For example, forthe category of weather, the context attributes can be defined byregion, location, time of day or season. The fuzzy term partitionprogram may produce one partition or more than one partitioncorresponding to one attribute or more than one attribute (e.g.,location and season).

As more data is received and processed, an additional level ofgranularity to the existent or new attributes may be added. For example,the attribute of region with fuzzy values (i.e., fuzzy terms) mapped toa large region may be extended to smaller, town based, regions as moredata is provided. The fuzzy term partition program may use, for example,a statistical method that averages individual usage when collecting datafor building a fuzzy partition.

Applying the fuzzy partitions at runtime may include receiving an inputand detecting the context of the conversation. Then a correspondingfuzzy partition may be applied to the context for mapping the used fuzzyterm to correspond to one or more crisp values. The runtime may includean implementation process that may provide recommendations to a user todescribe the intent of the conversation using crisp values. For example,someone from Canada would like to visit a warm place and provides aninput into a computing device using the fuzzy term warm. The program maydetect that the person asking for a warm destination resides in Canadaand a temperature range considered to be warm for a person living inCanada is a crisp value range of A→B. The program may then providerecommend places within an A→B temperature range. The person may use afuzzy term and the person may receive individualized data applying towarm destinations.

Referring to FIG. 1, an exemplary networked computer environment 100 inaccordance with one embodiment is depicted. The networked computerenvironment 100 may include a computer 102 with a processor 104 and adata storage device 106 that is enabled to run a software program 108and a fuzzy term partition program 110 a. The networked computerenvironment 100 may also include a server 112 that is enabled to run afuzzy term partition program 110 b that may interact with a database 114and a communication network 116. The networked computer environment 100may include a plurality of computers 102 and servers 112, only one ofwhich is shown. The communication network 116 may include various typesof communication networks, such as a wide area network (WAN), local areanetwork (LAN), a telecommunication network, a wireless network, a publicswitched network and/or a satellite network. It should be appreciatedthat FIG. 1 provides only an illustration of one implementation and doesnot imply any limitations with regard to the environments in whichdifferent embodiments may be implemented. Many modifications to thedepicted environments may be made based on design and implementationrequirements.

The client computer 102 may communicate with the server computer 112 viathe communications network 116. The communications network 116 mayinclude connections, such as wire, wireless communication links, orfiber optic cables. As will be discussed with reference to FIG. 4,server computer 112 may include internal components 902 a and externalcomponents 904 a, respectively, and client computer 102 may includeinternal components 902 b and external components 904 b, respectively.Server computer 112 may also operate in a cloud computing service model,such as Software as a Service (SaaS), Platform as a Service (PaaS),Analytics as a Service (AaaS) or Infrastructure as a Service (IaaS).Server 112 may also be located in a cloud computing deployment model,such as a private cloud, community cloud, public cloud, or hybrid cloud.Client computer 102 may be, for example, a mobile device, a telephone, apersonal digital assistant, a netbook, a laptop computer, a tabletcomputer, a desktop computer, or any type of computing devices capableof running a program, accessing a network, and accessing a database 114.According to various implementations of the present embodiment, thefuzzy term partition program 110 a, 110 b may interact with a database114 that may be embedded in various storage devices, such as, but notlimited to a computer/mobile device 102, a networked server 112, or acloud storage service.

According to the present embodiment, a user using a client computer 102or a server computer 112 may use the fuzzy term partition program 110 a,110 b (respectively) to build and apply fuzzy term partitions. The fuzzyterm partition method is explained in more detail below with respect toFIGS. 2A, 2B and 3.

Referring now to FIGS. 2A and 2B, an operational flowchart illustratingthe exemplary fuzzy term partition building process 200 used by thefuzzy term partition program 110 a, 110 b according to at least oneembodiment is depicted.

At 202, data is received. Data may be obtained from a corpus or adatabase (e.g., database 114). Information previously stored on adatabase and information flowing into a database (i.e., real-time inputto a database) may be obtained by the fuzzy term partition program 110a, 110 b. Data may include information obtained from various sources,such as social media accounts, Internet of Things (IoT) devices,sensors, audio files (e.g., way or mp3), video files (e.g., avi, flv ormp4), websites and user profiles. For example, a database may storeinformation relating to the weather and the weather data may be sent viaa communication network 116 from an IoT sensor device. The weather datamay also have been previously stored on a database and the informationmay be accessible to the fuzzy term partition program 110 a, 110 b.

Data may also be received by verbal communication or by type-writtencommunication. Verbal communication may be provided by an individualspeaking into a device (e.g., computer 102) or by a device that receivesthe communication. A conversation or utterance may be captured byvarious computing devices, for example, a device with a microphone. Somedevices may include, for example, a smart phone, a smart watch, a tabletor a computing device (e.g., computer 102), all of which may contain amicrophone. Type-written communication may be provided by an individualusing a computing device to type a message, for example, a text message,an email message or a social media post.

At 204, a fuzzy category taxonomy is built. A category taxonomy may bebuilt by defining the fuzzy terms and the fuzzy term attributes. Thefuzzy term attributes may include associating the fuzzy terms with acategory. A category may be defined with attributes mapped to theextracted fuzzy term used in a conversation. For example, the fuzzyterms cold, warm or hot may have an associated attribute of temperature.A category taxonomy may define the category and may also define thefuzzy term people use to describe the category. For each extracted fuzzyterm, the fuzzy term partition program 110 a, 110 b may group the fuzzyterms into a category. For each communication that contains a fuzzyterm, building the category taxonomy may include associating a categoryto each extracted fuzzy term. The extracted fuzzy terms may then begrouped into one category or multiple categories based on the context ofthe fuzzy term.

A category may contain a fuzzy term dictionary. For example, a productcategory may contain the fuzzy terms cheap, expensive, reasonable, agood deal, and other phrases extracted from conversations anddictionaries. After a fuzzy term has been categorized, the fuzzy termmay hold a different context or a different crisp value associated withthe fuzzy term for a particular region. For example, a person may view aparticular product price as expensive in one region and a person in adifferent region may view the same product price as reasonable or cheap.

Next, at 206, the fuzzy category classifier is implemented. The fuzzycategory classifier may be trained to detect the phrases that containfuzzy categories. The fuzzy term partition program 110 a, 110 b maybuild and implement a classifier to detect if a fuzzy term has been usedin an utterance or a conversation. The category of the utterance orconversation may be identified and the fuzzy term partition program 110a, 110 b may compute a higher confidence score for utterances with fuzzyterms. The classifier may be built by training a language model based onlabeling particular utterances based on the trained data. A trainedlanguage model may include, for example, a set of keywords and thekeyword frequencies (i.e., the number of time the keywords are used inan utterance) for a specific category and the category may include aunigram language model. The classifier may be trained to detectutterances of a category. For example, an utterance of the categoryweather may be detected by the classifier. The classifier may provideresults for future outputs the fuzzy term partition program 110 a, 110 bmay use to identify fuzzy terms. Correspondingly, the sentence,conversation or utterance may be labeled by a category.

Then, at 208, the fuzzy entity extractor is built. The fuzzy termpartition program 110 a, 110 b may use the fuzzy term dictionary and thefuzzy term categories to build the entity extractor. The fuzzy termpartition program 110 a, 110 b may build and train the fuzzy extractorto detect and extract fuzzy terms that are relevant to the identifiedcategory. The fuzzy term extractor may know (i.e., have access to) thefuzzy term categories from step 204. The fuzzy term partition program110 a, 110 b may find related fuzzy terms within a category by using thefuzzy term dictionary to extract the latest fuzzy terms. A fuzzy termentity may include a fuzzy term and the fuzzy term's attributes. Thefuzzy entity extractor may extract the fuzzy term and associate thefuzzy term with the fuzzy term attributes available in, for example, thecategory dictionary (e.g., the fuzzy term cold is associated with theattribute temperature).

For example, a normal daily utterance about the weather may reference acold day. The term cold day may provide context data for the fuzzy termwhere the context may be described as regional, demographical orprofessional. A professional context may, for example, include ascenario when weather may affect a person who works outside. The contextto build the entity extractor may depend on the usage or implementationof the fuzzy term partition program 110 a, 110 b. Weather data may, forexample, be important to regional, demographical or professionalcontexts at a certain time of the day to associate a crisp value to afuzzy term. The fuzzy term partition program 110 a, 110 b may identify,for example, the context data for regional weather and may obtain theweather data for the regions from a weather data application. Theobtained weather data may provide temperatures for a particular context(e.g., region) at a particular time. The context of region may beobtained from data from the region where the conversation or utterancehappened and the weather data may be tracked for a particular time ofday and be associated with the stated fuzzy terms.

Another example may include a conversation of a context of twoindividuals conversing and one states that the temperature outside is20° C. and the current temperature may be too cold to go swimming or toocold to go to the beach. The fuzzy term partition program 110 a, 110 bmay identify the category weather and extract the fuzzy term cold withan attribute of temperature.

At 210, an association map is built. An association map may associatefuzzy terms with various usage contexts. One fuzzy term may have anassociation with one or more contexts. Each fuzzy term context may bepartitioned and associated with different contexts, such as location,region, culture, demographic, professional, personal preferences, grouppreferences or age of the individuals. Since contexts may vary from asingle fuzzy term, an association map is built to correspond each fuzzyterm with the relevant associations.

Then, at 212, the fuzzy partitions for various fuzzy categories areproduced. For each fuzzy term, a fuzzy category may be built by apartition. The partition may contain attributes, context and values thatare crisp values of mapped fuzzy terms. The value (i.e., crisp value) ofthe fuzzy term for a particular context may have a particular valuebetween A→B.

For example, a built fuzzy partition may categorize data as shown inTable 1.

TABLE 1 Fuzzy Partition Examples Category → Product Category → TrafficFuzzy Term → Cheap, Expensive, Fuzzy Term → Rush-hour, Congest- GoodDeal ed, Traffic Jam, Bumper to Bumper Attribute → Pricing Attribute →density Context Data → State, City Context Data → Location, Street CrispValue → $A→$B or {$A, $B} Crisp Value → A→B or {A, B} Category → WeatherCategory → Route Description Fuzzy Term → Cold, Warm, Hot Fuzzy Term →Near, Close, Far Attribute → Temperature Attribute → feet, miles, metersContext Data → Region, Location Context Data → Location Crisp Value → A°C. →B° C. or Crisp Value → A meters→B meters {A° C., B° C.} or {Ameters, B meters}

An example of a single fuzzy term having more than one context mayinclude the fuzzy term cool. Cool may be used to describe a weathercategory with a regional context and cool may also be described as aproduct category with a product likability context.

Previous steps from FIG. 2A (i.e., steps 202 through 212) provide oneembodiment of implementation steps to build fuzzy partitions. Subsequentsteps from FIG. 2B (i.e., steps 214 through 222) provide one embodimentthe fuzzy term partition program 110 a, 110 b may use to produce fuzzypartitions for various fuzzy categories as shown in Table 1.

At 214, the corpus of conversations and utterances are processed. Foreach fuzzy partition produced at 212, the fuzzy term partition program110 a, 110 b may process a corpus (e.g., a database 114) ofconversations and utterances. Processing a corpus of conversations usingpartitions may provide boundaries for each different context and maybuild more associations as more conversations are processed. The fuzzyterm partition program 110 a, 110 b may apply the fuzzy categoryclassifier to detect the utterances and conversations related to thespecific analyzed category. For example, detecting the weather-relatedutterances.

Next, at 216, the value for fuzzy term entities from conversations areextracted. Fuzzy entities may be generated by the fuzzy entityextractor. The fuzzy entity extractor may extract a fuzzy term andassociate the fuzzy term with attributes available in, for example, thecategory dictionary (e.g., the fuzzy term cold is associated with theattribute temperature). Fuzzy terms are identified in a conversation aswords that may, for example, be subjective.

At 218, the value for fuzzy terms from relevant context data isextracted. The relevant context value may be extracted by establishingthe source of the conversation, such as the location where the personmaking the comment or utterance lives. Location may be retrieved, forexample, by acquiring data on a social media profile or by acquiringcoordinate data from a global positioning system (GPS). Fuzzy termvalues may be a numeric value associated with, for example, thetemperature of the region the utterance was made.

Next, at 220, the context fuzzy partitions are produced and updated. Foreach conversation or message, the fuzzy term partition program 110 a,110 b may produce the context of a fuzzy partition or may update thecontext of a fuzzy partition. The fuzzy term partition program 110 a,110 b may continuously be adding to or updating the fuzzy partitions asmore data is received and processed. Data may be received as input dataat step 202 and data may also be processed using stored data on a corpus(e.g., a database 114). Data may also be received and processed inreal-time, for example, from sensors, microphones, computing devices,smart phones, smart watches, tablets or IoT devices. Other data sourcesmay include, for example, websites or a user's profile data. As data isreceived and processed, the fuzzy partitions may be updated with thelatest extracted values.

Then, at 222, weights to the extracted fuzzy values are applied.Different weights may be applied to the extracted fuzzy values based onthe person's expertise level relevant to the referenced fuzzy term. Forexample, if individual A is conversing about the weather in a regionindividual A has resided for a long duration of time, determined bychecking a public records database, individual A's assigned weight maybe higher than individual B, when individual B has resided in the sameregion for less time than individual A.

Referring now to FIG. 3, an operational flowchart illustrating theexemplary fuzzy term partition application process 300 used by the fuzzyterm partition program 110 a, 110 b according to at least one embodimentis depicted.

At 302, an input is received and processed. Data may be received byverbal communication or by type-written communication. Verbalcommunication may be provided by an individual speaking into a device orby a device that receives the communication. A conversation or utterancemay be captured by various computing devices, for example, a device witha microphone. Some devices may include, for example, a smart phone, asmart watch, a tablet or a computing device (e.g., computer 102), all ofwhich may contain a microphone. Type-written communication may beprovided by an individual using a computing device to type a message,for example, a text message, an email message or a social media post.NLP may be used to process the received input.

Next, at 304, the usage context is analyzed. The usage context may beretrieved from, for example, the location of the person who iscommunicating with fuzzy terms. The context may be analyzed usingpreviously created dictionaries, categories or classifiers processed andbuilt by the fuzzy term partition program 110 a, 110 b.

Then, at 306, fuzzy partition values to fuzzy terms and contextualrelevancy are applied. Contextual relevancy may associate the statedfuzzy term with, for example, a particular region or demographic.Contextual relevancy may be obtained from the previously builtpartitions at step 212. Associating the fuzzy term with the propercontext using built partitions may provide relevant data, such asattributes or crisp data.

At 308, output values for fuzzy terms are provided. An output may beprovided on a computing device (e.g., computer 102) to a user. An outputmay, for example, be a voice alert or a type-written message in the formof a text message or an email message. The output may provide the userwith, for example, an answer to a question about finding warm vacationlocations during the winter.

It may be appreciated that FIGS. 2A, 2B and 3 provide only anillustration of one embodiment and do not imply any limitations withregard to how different embodiments may be implemented. Manymodifications to the depicted embodiment(s) may be made based on designand implementation requirements.

FIG. 4 is a block diagram 900 of internal and external components ofcomputers depicted in FIG. 1 in accordance with an illustrativeembodiment of the present invention. It should be appreciated that FIG.4 provides only an illustration of one implementation and does not implyany limitations with regard to the environments in which differentembodiments may be implemented. Many modifications to the depictedenvironments may be made based on design and implementationrequirements.

Data processing system 902, 904 is representative of any electronicdevice capable of executing machine-readable program instructions. Dataprocessing system 902, 904 may be representative of a smart phone, acomputer system, PDA, or other electronic devices. Examples of computingsystems, environments, and/or configurations that may represented bydata processing system 902, 904 include, but are not limited to,personal computer systems, server computer systems, thin clients, thickclients, hand-held or laptop devices, multiprocessor systems,microprocessor-based systems, network PCs, minicomputer systems, anddistributed cloud computing environments that include any of the abovesystems or devices.

User client computer 102 and network server 112 may include respectivesets of internal components 902 a, b and external components 904 a, billustrated in FIG. 4. Each of the sets of internal components 902 a, bincludes one or more processors 906, one or more computer-readable RAMs908 and one or more computer-readable ROMs 910 on one or more buses 912,and one or more operating systems 914 and one or more computer-readabletangible storage devices 916. The one or more operating systems 914, thesoftware program 108 and the fuzzy term partition program 110 a inclient computer 102, and the fuzzy term partition program 110 b innetwork server 112, may be stored on one or more computer-readabletangible storage devices 916 for execution by one or more processors 906via one or more RAMs 908 (which typically include cache memory). In theembodiment illustrated in FIG. 4, each of the computer-readable tangiblestorage devices 916 is a magnetic disk storage device of an internalhard drive. Alternatively, each of the computer-readable tangiblestorage devices 916 is a semiconductor storage device such as ROM 910,EPROM, flash memory or any other computer-readable tangible storagedevice that can store a computer program and digital information.

Each set of internal components 902 a, b also includes a R/W drive orinterface 918 to read from and write to one or more portablecomputer-readable tangible storage devices 920 such as a CD-ROM, DVD,memory stick, magnetic tape, magnetic disk, optical disk orsemiconductor storage device. A software program, such as the softwareprogram 108 and the fuzzy term partition program 110 a, 110 b can bestored on one or more of the respective portable computer-readabletangible storage devices 920, read via the respective R/W drive orinterface 918 and loaded into the respective hard drive 916.

Each set of internal components 902 a, b may also include networkadapters (or switch port cards) or interfaces 922 such as a TCP/IPadapter cards, wireless wi-fi interface cards, or 3G or 4G wirelessinterface cards or other wired or wireless communication links. Thesoftware program 108 and the fuzzy term partition program 110 a inclient computer 102 and the fuzzy term partition program 110 b innetwork server computer 112 can be downloaded from an external computer(e.g., server) via a network (for example, the Internet, a local areanetwork or other, wide area network) and respective network adapters orinterfaces 922. From the network adapters (or switch port adaptors) orinterfaces 922, the software program 108 and the fuzzy term partitionprogram 110 a in client computer 102 and the fuzzy term partitionprogram 110 b in network server computer 112 are loaded into therespective hard drive 916. The network may comprise copper wires,optical fibers, wireless transmission, routers, firewalls, switches,gateway computers and/or edge servers.

Each of the sets of external components 904 a, b can include a computerdisplay monitor 924, a keyboard 926, and a computer mouse 928. Externalcomponents 904 a, b can also include touch screens, virtual keyboards,touch pads, pointing devices, and other human interface devices. Each ofthe sets of internal components 902 a, b also includes device drivers930 to interface to computer display monitor 924, keyboard 926, andcomputer mouse 928. The device drivers 930, R/W drive or interface 918and network adapter or interface 922 comprise hardware and software(stored in storage device 916 and/or ROM 910).

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based email). Theconsumer does not manage or control the underlying cloud infrastructureincluding network, servers, operating systems, storage, or evenindividual application capabilities, with the possible exception oflimited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Analytics as a Service (AaaS): the capability provided to the consumeris to use web-based or cloud-based networks (i.e., infrastructure) toaccess an analytics platform. Analytics platforms may include access toanalytics software resources or may include access to relevantdatabases, corpora, servers, operating systems or storage. The consumerdoes not manage or control the underlying web-based or cloud-basedinfrastructure including databases, corpora, servers, operating systemsor storage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 5, illustrative cloud computing environment 1000is depicted. As shown, cloud computing environment 1000 comprises one ormore cloud computing nodes 100 with which local computing devices usedby cloud consumers, such as, for example, personal digital assistant(PDA) or cellular telephone 1000A, desktop computer 1000B, laptopcomputer 1000C, and/or automobile computer system 1000N may communicate.Nodes 100 may communicate with one another. They may be grouped (notshown) physically or virtually, in one or more networks, such asPrivate, Community, Public, or Hybrid clouds as described hereinabove,or a combination thereof. This allows cloud computing environment 1000to offer infrastructure, platforms and/or software as services for whicha cloud consumer does not need to maintain resources on a localcomputing device. It is understood that the types of computing devices1000A-N shown in FIG. 5 are intended to be illustrative only and thatcomputing nodes 100 and cloud computing environment 1000 can communicatewith any type of computerized device over any type of network and/ornetwork addressable connection (e.g., using a web browser).

Referring now to FIG. 6, a set of functional abstraction layers 1100provided by cloud computing environment 1000 is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 6 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 1102 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 1104;RISC (Reduced Instruction Set Computer) architecture based servers 1106;servers 1108; blade servers 1110; storage devices 1112; and networks andnetworking components 1114. In some embodiments, software componentsinclude network application server software 1116 and database software1118.

Virtualization layer 1120 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers1122; virtual storage 1124; virtual networks 1126, including virtualprivate networks; virtual applications and operating systems 1128; andvirtual clients 1130.

In one example, management layer 1132 may provide the functionsdescribed below. Resource provisioning 1134 provides dynamic procurementof computing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 1136provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 1138 provides access to the cloud computing environment forconsumers and system administrators. Service level management 1140provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 1142 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 1144 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 1146; software development and lifecycle management 1148;virtual classroom education delivery 1150; data analytics processing1152; transaction processing 1154; and fuzzy partition implementationand application 1156. A fuzzy term partition program 110 a, 110 bprovides a way to build and apply fuzzy term partitions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A method for building and applying fuzzy termpartitions, the method comprising: receiving a fuzzy term input;building a fuzzy category taxonomy based on the received input;implementing the built fuzzy category taxonomy into a fuzzy categoryclassifier; building a fuzzy term extractor based on the implementedfuzzy category classifier; building a fuzzy term association map basedon the built fuzzy term extractor; processing a plurality of wordsstored on a database; extracting a fuzzy term from the processedplurality of words; associating the extracted fuzzy term with aplurality of context data; producing a context data partition for theextracted fuzzy term based on the associated plurality of context data;and applying a weight to the extracted fuzzy term.
 2. The method ofclaim 1, further comprising: analyzing a usage context for the receivedfuzzy term input; applying a fuzzy partition value to the received fuzzyterm input; applying a contextual relevancy to the received fuzzy terminput; and providing an output based on the applied contextualrelevancy.
 3. The method of claim 1, wherein the weight applied to thefuzzy term is based on a level of expertise of a person speaking thefuzzy term or the person typing the fuzzy term.
 4. The method of claim1, wherein the context data partition is created for each fuzzy term. 5.The method of claim 4, wherein each fuzzy term has a plurality ofcontext data partitions created for the fuzzy term and associated withthe fuzzy term.
 6. The method of claim 4, wherein the context datapartition contains a set of data, and wherein the set of data ispartitioned as a category, the fuzzy term, an attribute, a plurality ofcontext data, and a crisp value.
 7. The method of claim 6, wherein thecrisp value is a specific value or a specific range of values thatdepict the fuzzy term.
 8. A computer system for building and applyingfuzzy term partitions, comprising: one or more processors, one or morecomputer-readable memories, one or more computer-readable tangiblestorage medium, and program instructions stored on at least one of theone or more tangible storage medium for execution by at least one of theone or more processors via at least one of the one or more memories,wherein the computer system is capable of performing a methodcomprising: receiving a fuzzy term input; building a fuzzy categorytaxonomy based on the received input; implementing the built fuzzycategory taxonomy into a fuzzy category classifier; building a fuzzyterm extractor based on the implemented fuzzy category classifier;building a fuzzy term association map based on the built fuzzy termextractor; processing a plurality of words stored on a database;extracting a fuzzy term from the processed plurality of words;associating the extracted fuzzy term with a plurality of context data;producing a context data partition for the extracted fuzzy term based onthe associated plurality of context data; and applying a weight to theextracted fuzzy term.
 9. The computer system of claim 8, furthercomprising: analyzing a usage context for the received fuzzy term input;applying a fuzzy partition value to the received fuzzy term input;applying a contextual relevancy to the received fuzzy term input; andproviding an output based on the applied contextual relevancy.
 10. Thecomputer system of claim 8, wherein the weight applied to the fuzzy termis based on a level of expertise of a person speaking the fuzzy term orthe person typing the fuzzy term.
 11. The computer system of claim 8,wherein the context data partition is created for each fuzzy term. 12.The computer system of claim 11, wherein each fuzzy term has a pluralityof context data partitions created for the fuzzy term and associatedwith the fuzzy term.
 13. The computer system of claim 11, wherein thecontext data partition contains a set of data, and wherein the set ofdata is partitioned as a category, the fuzzy term, an attribute, aplurality of context data, and a crisp value.
 14. The computer system ofclaim 13, wherein the crisp value is a specific value or a specificrange of values that depict the fuzzy term.
 15. A computer programproduct for building and applying fuzzy term partitions, comprising: oneor more computer-readable storage media and program instructions storedon at least one of the one or more tangible storage media, the programinstructions executable by a processor to cause the processor to performa method comprising: receiving a fuzzy term input; building a fuzzycategory taxonomy based on the received input; implementing the builtfuzzy category taxonomy into a fuzzy category classifier; building afuzzy term extractor based on the implemented fuzzy category classifier;building a fuzzy term association map based on the built fuzzy termextractor; processing a plurality of words stored on a database;extracting a fuzzy term from the processed plurality of words;associating the extracted fuzzy term with a plurality of context data;producing a context data partition for the extracted fuzzy term based onthe associated plurality of context data; and applying a weight to theextracted fuzzy term.
 16. The computer program product of claim 15,further comprising: analyzing a usage context for the received fuzzyterm input; applying a fuzzy partition value to the received fuzzy terminput; applying a contextual relevancy to the received fuzzy term input;and providing an output based on the applied contextual relevancy. 17.The computer program product of claim 15, wherein the weight applied tothe fuzzy term is based on a level of expertise of a person speaking thefuzzy term or the person typing the fuzzy term.
 18. The computer programproduct of claim 15, wherein the context data partition is created foreach fuzzy term.
 19. The computer program product of claim 18, whereineach fuzzy term has a plurality of context data partitions created forthe fuzzy term and associated with the fuzzy term.
 20. The computerprogram product of claim 18, wherein the context data partition containsa set of data, and wherein the set of data is partitioned as a category,the fuzzy term, an attribute, a plurality of context data, and a crispvalue.